TY - GEN
T1 - A survey on temporal reasoning for temporal information extraction from text
AU - Leeuwenberg, Artuur
AU - Moens, Marie Francine
N1 - Funding Information:
This work was supported by the ERC Advanced Grant CALCULUS (788506), and by the IWT-SBO project ACCUMULATE (150056).
Publisher Copyright:
© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
AB - Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
UR - http://www.scopus.com/inward/record.url?scp=85097332646&partnerID=8YFLogxK
U2 - 10.5555/3491440.3492152
DO - 10.5555/3491440.3492152
M3 - Conference contribution
AN - SCOPUS:85097332646
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 5085
EP - 5089
BT - Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
A2 - Bessiere, Christian
PB - International Joint Conferences on Artificial Intelligence
T2 - 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Y2 - 1 January 2021
ER -